Integrating Satellite Remote Sensing and Dynamic GIS for Urban Flood Risk Modelling in Ulaanbaatar City

Abstract. Climate change has intensified the frequency and severity of natural disasters globally. In Ulaanbaatar, Mongolia, rapid urbanization and high population density have heightened susceptibility to flood disasters, particularly those associated with extreme rainfall events. Historical data reveal a pronounced increase in flood occurrences, with 60% of the 45 recorded floods between 1995 and 2021 occurring from 2000 to 2009. The 2020 flood event alone breached levees at six locations and damaged 60 structures, underscoring the imperative for robust flood risk assessment and mitigation strategies. This study employs high-resolution remote sensing data within a GIS framework to conduct a comprehensive flood risk assessment. Diverging from conventional hydrological models (e.g., SWMM, HEC-RAS), an overlay weighted model is utilized for its efficacy in multi-criteria spatial analysis. Six parameters are integrated: land use/cover (LULC), elevation (12.5 m DEM), slope, watershed boundaries, flow direction, and precipitation (90 m resolution). Data sources include ALOS/PALSAR (DEM), Sentinel-2 (LULC), and CRU TS v4 (precipitation). Thematic analysis identifies Khan-Uul and Sukhbaatar districts as high-risk zones due to low-lying topography, while steep-sloped areas exhibit lower vulnerability. The risk map classifies 19.66% (775.7 km2) as "very high risk" (e.g., Tuul and Selbe riversides) and 25.5% (1,006.89 km2) as "high risk," corroborating historical events like the 1966 flood (103.5 mm rainfall, 43% of annual average).